code: def iterative_fit(self, X, y, n_iter=1, refit=False):
      from sklearn.ensemble import RandomForestRegressor
  
      if refit:
          self.estimator = None
  
      if self.estimator is None:
          self.n_estimators = int(self.n_estimators)
          if check_none(self.max_depth):
              self.max_depth = None
          else:
              self.max_depth = int(self.max_depth)
  
          self.min_samples_split = int(self.min_samples_split)
          self.min_samples_leaf = int(self.min_samples_leaf)
  
          self.max_features = float(self.max_features)
  
          self.bootstrap = check_for_bool(self.bootstrap)
  
          if check_none(self.max_leaf_nodes):
              self.max_leaf_nodes = None
          else:
              self.max_leaf_nodes = int(self.max_leaf_nodes)
  
          self.min_impurity_decrease = float(self.min_impurity_decrease)
  
          self.estimator = RandomForestRegressor(
              n_estimators=n_iter,
              criterion=self.criterion,
              max_features=self.max_features,
              max_depth=self.max_depth,
              min_samples_split=self.min_samples_split,
              min_samples_leaf=self.min_samples_leaf,
              min_weight_fraction_leaf=self.min_weight_fraction_leaf,
              bootstrap=self.bootstrap,
              max_leaf_nodes=self.max_leaf_nodes,
              min_impurity_decrease=self.min_impurity_decrease,
              random_state=self.random_state,
              n_jobs=self.n_jobs,
              warm_start=True,
          )
      else:
          self.estimator.n_estimators += n_iter
          self.estimator.n_estimators = min(
              self.estimator.n_estimators, self.n_estimators
          )
  
      if y.ndim == 2 and y.shape[1] == 1:
          y = y.flatten()
  
      self.estimator.fit(X, y)
      return self
  
file_path: auto-sklearn/autosklearn/pipeline/components/regression/random_forest.py
imports: from typing import Optional
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformFloatHyperparameter, UniformIntegerHyperparameter, UnParametrizedHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm, IterativeComponent
  from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA
  from autosklearn.util.common import check_for_bool, check_none
label_desc: Use image node_id 4 for calling the RandomForest obj's underlying member method code with example usage: obj.iterative_fit(X, y, n_iter, refit) and returns: self